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Abstract
IntroductionAbnormal tumor vasculature impairs oxygen delivery and induces hypoxia, contributing to treatment resistance and poor prognosis in non-small cell lung cancer (NSCLC). Although radiation therapy can modulate tumor vessels, its effects vary widely due to vascular heterogeneity. Therefore, a reliable and noninvasive method to quantify vascular abnormality is needed to better predict treatment outcomes.MethodsWe developed a deep learning-based imaging biomarker, the Vessel Risk Score (VRS), to quantify tumor vascular abnormality from contrast-enhanced CT scans. Trained on multi-institutional data from 126 NSCLC patients treated with hypofractionated radiotherapy, the model learned vascular morphology patterns from tumor-vessel images. Using these learned patterns, vascular heterogeneity was quantified as the distributional difference from normal vessel morphology. The generalizability of VRS was then evaluated in an external cohort of 128 early-stage NSCLC patients who underwent stereotactic body radiotherapy (SBRT).ResultsVRS showed significantly better prediction of SBRT radiation therapy response compared to vessel density. The VRS of the responder group was 0.494 (95% CI: 0.47-0.52), significantly lower than the non-responder group's 0.578 (95% CI: 0.54-0.62). Additionally, patients with high VRS showed significantly shorter PFS compared to those with low VRS (p < 0.05). In Cox multivariate analysis, VRS emerged as the only significant predictor among vessel density and other clinical variables (p < 0.05).ConclusionThe proposed AI-derived VRS provides a noninvasive and reproducible measure of tumor vascular abnormality, offering improved prediction of radiation therapy response and prognosis compared with vessel density. This approach may extend to prognostic assessment in other cancer types where vascular morphology plays a critical role.

Affiliations

Jun Hyeong Park 1 2, Jun Hyeok Lim 3, Seonhwa Kim 1, Chul-Ho Kim 4, Seulgi You 5, Jeong-Seok Choi 6, Jae Won Chang 7, Dongil Park 8, Myung-Won Lee 9, Sup Kim 10, In Young Jo 11, Hyung Kwon Byeon 12, Ki Nam Park 12, Byung-Joo Lee 13, Sung-Chan Shin 13, Yong-Il Cheon 13, Jaesung Heo 1
1Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea.
2Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, Republic of Korea.
3Division of Pulmonology, Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of Korea.
4Department of Otolaryngology, Ajou University School of Medicine, Suwon, Republic of Korea.
5Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea.
6Department of Otorhinolaryngology-Head and Neck Surgery, Inha University College of Medicine, Incheon, Republic of Korea.
7Department of Otolaryngology-Head and Neck Surgery, Chungnam National University Hospital, Daejeon, Korea.
8Division of Pulmonary, Allergy and Critical Care Medicine, Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Korea.
9Division of Hematology and Oncology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Korea.
10Department of Radiation Oncology, Chungnam National University Hospital, Daejeon, Korea.
11Department of Radiation oncology, Soonchunhyang University, Cheonan Hospital, Cheonan, Republic of Korea.
12Department of Otorhinolaryngology-Head and Neck Surgery, Soonchunhyang University, Cheonan, Republic of Korea.
13Department of Otorhinolaryngology - Head and Neck Surgery, College of Medicine, Pusan National University and Biomedical Research Institute, Pusan National University Hospital, Busan, Korea.